Mining of distributed scientific data for enriched valuation

ABSTRACT

Methods and systems are described that detail the rapid validation of scientific products and scientist consumer information derived from multiple science/research based sources, such as peer-reviewed scientific publications and online resources, for example, as well as any other available tasked forum or venue. The refined information will facilitate unique product evaluation opportunities via an accessible website/forum, trade shows, etc., and provide previously unknowable sales leads, market intelligence, and targeted advertising opportunities for product manufacturers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/305,471, filed Jun. 16, 2014, and issued as U.S. Pat. No. 11,074,628,which is a continuation of U.S. patent application Ser. No. 12/784,258,filed May 20, 2010, and issued as U.S. Pat. No. 8,793,152. ApplicationSer. No. 12/784,258 claims the benefit of U.S. Provisional PatentApplication No. 61/179,952, titled “Mining Of Distributed ScientificData For Enriched Product/Contact Valuation,” filed May 20, 2009, thecontents of which are hereby incorporated by reference in its entirety.

FIELD

This invention is related to scientific and product informationexchange. More particularly, this invention is related to methods andsystems for aggregating product information from various science-relatedforums and providing enhanced relevance/value feedback mechanisms foridentified products/contacts.

BACKGROUND

Life researchers and physicians work to discover cures for diseases,develop drugs, and learn about our world. Their job cycle contains theunusual requirement of publishing their discoveries in scientific andmedical journals as well as the need to compose grant applications toacquire funds from both public and private sources. Increasingly, lifescientists use Internet Web sites (“web”) to discover the tools and/orproducts necessary to complete their research as well as communicatewith their peers. The best source of tools and/or product information iswithin peer-reviewed scientific publications in which the publishingscientist is required to disclose the exact products (name andmanufacturer) used to perform a particular scientific application. Thetools and/or products are listed in detailed “scientific methods”sections in the publication which allows other scientists to repeat andvalidate the discovery. This information, however, is embedded in the“Web” and also in private subscription-based forums, making it difficultfor any single researcher to find a complete assessment of asought-after tool or product, or even tools or products that would havea bearing on his project that he may not be aware of.

On the other end of the spectrum, life science product manufacturers areseeking to identify scientists to market their products to. To reachscientists, manufacturers rely on trade shows, product listing sites,advertising in print scientific journals, online advertising, andpurchasing lists of validated email lists. Still, it is often difficultor impossible to evaluate the validity of a “sales lead” discovered withthese methods. Again the information needed lies hidden in the largeunstructured data set of scientific publications, grant applications,and Internet Web sites.

Therefore, there has been a long standing need in the scientificcommunity as well in the scientific vendor community for a mechanismthat efficiently provides concise and relevant information on productuse, viability, success and so forth. Systems and methods for addressingthese and other needs in the communities are elucidated below.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the claimed subject matter. Thissummary is not an extensive overview, and is not intended to identifykey/critical elements or to delineate the scope of the claimed subjectmatter. Its purpose is to present some concepts in a simplified form asa prelude to the more detailed description that is presented later.

In one aspect of the present disclosure, a method for mining distributedscientific data for enriched product or contact valuation is provided,comprising:

obtaining at least one of product data and contact data from a pluralityof independently segregated sources using an automatic searching tool;associating the obtained data with keywords relevant to the at least oneproduct and contact data using an automatic association tool; applying acomputerized semantic refinement algorithm to the associated data toobtain refined data; and automatically parsing the refined data to anapplicable product database and an applicable contact database, whereinthe databases contain concise information regarding at least one of aproduct summary, product review, top citing publications for theproduct, web mentions, topic summary, related topics, other products ina family, related products, competing products, names of top expertsusing the product, current market share, market share trends, andScience 2.0 citations,wherein the concise information correlates at least one of productefficacy and product use with the obtained data for efficient valuationthereof.

In another aspect of the present disclosure, a system for miningdistributed scientific data for enriched product or contact valuation isprovided, comprising: means for obtaining at least one of product dataand contact data from a plurality of independently segregated sourcesusing an automatic searching tool; means for associating the obtaineddata with keywords relevant to the at least one product and contact datausing an automatic association tool; means for applying a computerizedsemantic refinement algorithm to the associated data to obtain refineddata; and means for automatically parsing the refined data to anapplicable product database and an applicable contact database, whereinthe databases contain concise information regarding at least one of aproduct summary, product review, top citing publications for theproduct, web mentions, topic summary, related topics, other products ina family, related products, competing products, names of top expertsusing the product, current market share, market share trends, andScience 2.0 citations, wherein the concise information correlates atleast one of product efficacy and product use with the obtained data forefficient valuation thereof.

In yet another aspect of the present disclosure, a system for miningdistributed scientific data for enriched product and/or contactvaluation is provided, comprising: a communication network; acomputerized device connected to the communication network, executinginstructions for: obtaining at least one of product data and contactdata from a plurality of independently segregated sources on thecommunication network using an automatic searching tool; associating theobtained data with keywords relevant to the at least one product andcontact data using an automatic association tool; applying acomputerized semantic refinement algorithm to the associated data toobtain refined data; and automatically parsing the refined data to anapplicable product database and an applicable contact database, whereinthe databases contain concise information regarding at least one of aproduct summary, product review, top citing publications for theproduct, web mentions, topic summary, related topics, other products ina family, related products, competing products, names of top expertsusing the product, current market share, market share trends, andScience 2.0 citations, wherein the concise information correlates atleast one of product efficacy and product use with the obtained data forefficient valuation thereof.

In yet another aspect of the present disclosure, a non-transitorycomputer-readable medium is provided, comprising: programming code forobtaining at least one of product data and contact data from a pluralityof independently segregated sources using an automatic searching tool;programming code for associating the obtained data with keywordsrelevant to the at least one product and contact data using an automaticassociation tool; programming code for applying a computerized semanticrefinement algorithm to the associated data to obtain refined data; andprogramming code for automatically parsing the refined data to anapplicable product database and an applicable contact database, whereinthe databases contain concise information regarding at least one of aproduct summary, product review, top citing publications for theproduct, web mentions, topic summary, related topics, other products ina family, related products, competing products, names of top expertsusing the product, current market share, market share trends, andScience 2.0 citations, wherein the concise information correlates atleast one of product efficacy and product use with the obtained data forefficient valuation thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages of the invention will now bedescribed with reference to the drawings of certain preferredembodiments, which are intended to illustrate and not to limit theinvention.

FIG. 1 is a high-level client-server-Internet diagram.

FIG. 2 is a simple diagram 20 illustrating the compartmentalization ofcurrent product information among different forums.

FIG. 3 is a high level block diagram illustrating an exemplary systemand approach to addressing the deficiencies described in FIG. 2.

FIG. 4 is a high-level architectural drawing illustrating various flowsof extracted/transformed information and their control as well asoptions for commercial exploitation of that information.

FIG. 5 is a diagram showing an example of exemplary keyword associationsthat facilitate an exemplary semantic enrichment process.

FIG. 6 is a detailed flow diagram illustrating the exemplary entry,semantic enrichment, and data display of a life science product.

FIG. 7 is a flow diagram illustrating the entry, semantic enrichment,and data display of life scientist consumer data.

FIGS. 8A-B are exemplary screen shots of a representative refined dataset for an example product (Lipofectamine 2000) for semantic enrichmentusing processes.

FIG. 9 is a screen display illustrating an HTML page displaying relevantproduct data for the example product (MiniOpticon).

FIG. 10 is an exemplary screen display illustrating an HTML page withina blog environment.

DETAILED DESCRIPTION

In various embodiments described below, methods and systems are providedfor aggregating/mining/distilling product-based information tofacilitate informed and rapid purchases of products. This informationcan be facilitated by a primary entity, such as in an exemplary websitethat provides information hosting capabilities. Via this website,scientists can also communicate, find jobs and scientific conferences,research and discover products, and ultimately purchase products to aidtheir experimentation.

While the below descriptions are cast in the context of life sciences,physicians, and researchers, it is explicitly understood that thevarious embodiments and disclosures herein may be utilized in otherdisciplines, as found applicable. Therefore, the exemplary discussionsprovided below can be modified to be applicable to other disciplines andcategories of professions, without departing from the spirit and scopeof this disclosure.

INTRODUCTION

As alluded to above, information containing the use and/or success ofvarious products presented in scientific publications and in variousscientific forums is usually dispersed in seemingly unrelated or onlytenuously related articles. Therefore, this presents a major bottleneckfor scientists determining the “correct” products to utilize in aspecific scientific application, as well as the fact that science dealsin exacting minutia (e.g., a product that works correctly for analyzingone virus may not work for another). To find products, scientists mustconduct extensive research such as utilizing peer-to-peer contacts,trade shows, trade magazines, company websites, product listingwebsites, and online professional networks. However, it is apparent tothe inventors that the source of product information lies within thebevy of peer-reviewed scientific publications—in which the publishingscientist is required to disclose the exact products (name andmanufacturer) used to perform a particular scientific application. Theproducts are listed in detailed “scientific methods” sections that areessential to allow other scientists to repeat and validate thediscovery.

Additionally, the majority of published research articles areproprietary and accessible by subscription-only, making theminaccessible to modern computerized search tools. Furthermore, thesearticles represent a large, unstructured data set even if they arefreely accessible on the Internet (open access). Thus, scientists spendcountless hours reading Experimental Methods sections of publications inorder to find products used successfully by their peers, if theinformation is freely available. For subscription or fee-basedinformation, researchers at small Biotech companies and at lessfortunate research institutes are usually unable to obtain thepublications due to the high subscription/fee costs involved.

Conversely on the commercial side, product manufacturers are seeking toidentify scientists to market their products to. Their approach istypically to rely on trade shows, product listing sites, advertising inprint scientific journals, online advertising, and purchasing lists ofvalidated emails lists. Another problem commonly faced by life scienceproduct manufacturers is an inability to efficiently attract potentialscientific consumers to their Web sites.

DESCRIPTION

The present disclosure describes the implementation and use of rapidvalidation of scientific products and scientist consumer informationderived from peer-reviewed scientific publications and online resources(e.g., blogs, discussion boards, university websites, patents, etc), forexample, as well as any other available tasked forum or venue. A hub forimplementation of the various embodiments described herein can beresourced from an exemplary primary entity or Web site, such as onesponsored by SCIENTISTSOLUTIONS.COM. The obtained refined andextrapolated data set will facilitate unique opportunities via anaccessible website/forum and trade shows, etc., such as presented by theexemplary website, for example. Concomitantly, the garnered informationand data points can provide previously unknowable “sales leads,” marketintelligence, and targeted advertising opportunities for life scienceproduct manufacturers through the same outlets. The systems and methodscan be implemented in part by semantic software tools that refineproduct data or a scientist's information to discover and deriveconnections from scientific publications and online resources and othersearchable venues.

In one embodiment, another available feature is the refinement ofscientific product information to facilitate product sales starting withonly a product name and SKU number, as one example. Life science productnames can be easily identified by mention in online discussions, such ason the exemplary SCIENTISTSOLUTIONS.COM's scientific community Web site,information requests on the SCIENTISTSOLUTIONS.COM's e-commerce Website, or by direct submission of product lists from life science productmanufacturers, and so forth. The product names can then be categorizedand tagged with a proprietary list of keywords associated with similarscientific products. These keywords permit the semantic enrichment ofproduct information beyond a simple description provided by themanufacturer. To accomplish this, each product can be designated with aunique identifier code. In some instances, it may be submitted to asemantic enriching source such as provided by Parity Computing Inc., forexample. The semantic enriching can be accomplished by miningrelationships to the product name by linking data from scientificpublications and the Internet, as several possible resource examples.The resulting mined information can formulate relationships betweenproducts and between users/experiments, and enable higher degrees ofcorrelation and information transparency. As one example, from thislarge unstructured data set, a product profile can be derived includingexperimental application details, the number and relevancy ofpublications mentioning the product, identification of competingproducts, market share for the product, as well as identification ofscientists using the products by name and scientific field (e.g.,cancer, diabetes, neurosurgery), and so forth. The refined productinformation can be ultimately stored in a database server (may besecure) that can be recalled using each product's unique identifiercode, if so designed.

In another embodiment, another aspect is that the refined productinformation can be displayed via code, referred to generally as a “webservices call,” into widgets that can be coded to appear on any Website. This will allow the primary entity or owner of the information toutilize discreet blocks of product information to generate revenue,according to various methods described below.

In another embodiment, software running on an e-commerce site (forexample, on SCIENTISTSOLUTIONS.COM) can display a portion of the derivedproduct information to supply scientists with the appropriate data tomake an informed purchase decision. That is, based on the quality andvalue of information provided to a recipient, the recipient can beushered into a purchase option. For example, it is understood by theinventors that the publication in an Experimental Methods section of apeer-reviewed scientific publication represents the ultimateverification of a given product's usefulness to a scientist since, bynecessity, the product must have functioned properly for otherscientists. Furthermore, the total number of scientists publishingmanuscripts citing a given product provides a useful measure to comparecompetitive products. Thus, a list of publications citing a particularproduct(s) would appear along with the product information on anexemplary Web page, referred to here as a “product page.” A scientist,viewing this comprehensive “history” of the product(s), can then rapidlydiscover the correct product to perform a given experiment and is morelikely to make a purchase via a “Buy Now” link or equivalent from theproduct information page.

The tabulation of Experimental Methods or an equivalent could besearched based on a scientist's request, to enable the scientist toquickly and easily view all or a limited set of products that were usedwith a selected search constraint. That is, a scientist could search forall products that were in Experimental Methods related to a particularform of treatment, for example. And based on that result the scientistwill be provided information as to which product was more popular, usedwith higher success rates, etc.

Consequently, the above collation of information, having metricsassessed thereto, about a given product(s) married with an option toimmediately purchase the product(s), provides a degree ofinformation-to-purchase transference not available in the current stateof art regarding products found in the scientific community and othercommunities.

In another embodiment, an option may be provided to enable the scientistto seek more detailed information via a “click through” or “referrallink” from the product page to a manufacturer's or product reseller'sWeb site. Scientists may also use the same publication information tomatch products to highly specific experimental applications. Forexample, a scientist could discover whether a particular product waspreferred by others working on a specific cell type, disease state, orwithin their own institution. That is, as one example, if the scientistis aware of another scientist's work in a particular area, he can see ifthat particular product was used by the other scientist. Thisinformation would provide both validation of product acceptance/success,or in the converse, if the other scientist's results were less thanfavorable, an opportunity to see if that product could have contributedto the less than favorable result, either by cross-referencing otherusers of that product and/or other scientists using a dissimilarproduct.

In one embodiment, the web services data recall may be configured toalso allow a portion of the data to be ported to business partners ofthe primary entity/SSI. For example, product manufacturers could displaya product widget that enables scientists to make better-informeddecisions about a product directly on a “product page” hosted on theirWeb site. Further, scientific Web sites, blogs, news sites, scientificjournals, etc. can become affiliate partners of the primary entity tomarket and sell products mentioned in their content with a similarwidget. The affiliate partners can in turn be paid a commission based onthe referral links back to the primary entity, directly to the productmanufacturer's Web site, or based on the actual purchase of a specificproduct.

As another embodiment, synergies obtained by collating the minedinformation can be extrapolated into or from a scientific onlinecommunity. For example, it is known that within the community discussionboards experiments are discussed in detail including the use of specificproducts. This represents a novel opportunity to promote products to aidscientists directly tied to key concepts and product names within theirposts using a data widget. Thus, these products and/or informationregarding these products can be presented for reading to a particularscientific discussion by utilizing the refined data and non-obviousconnections created by a semantic enrichment process. As a commercialembodiment, the primary entity featuring the discussion board(s) candirectly embed a “referral link” or “Buy Now” button within adiscussion. The same type of targeting can be utilized by affiliatewebsites of the primary entity to promote products as mentioned herein.These features and others are detailed below in the following Figures.

FIG. 1 is a high-level client-server-Internet diagram 10. Internet cloud12 is connected to various servers 14 and clients 16 either directly orindirectly via connections 15. The connections 15 may be hard line orwireless or any combination thereof. The servers 14 can hostindividually or in a distributed format, the primary entity's softwareand mechanisms for providing the features of the various embodimentsdiscussed above. In some instances, the primary entity's software andmechanisms may be hosted on a client machine 16, depending on design andmachine capabilities. The various servers 14 and clients 16 will becomputing stations with appropriate support hardware, such as diskdrives, processors, and so forth. It is understood that FIG. 1 is ageneral description of a client-server Internet diagram and thereforedoes not describe all the possible connections, hardware andarrangements for communication/processing hardware that may be utilized.Therefore, modifications may be made to the features of FIG. 1 with theunderstanding that they are within the scope and purview of the variousexemplary embodiments described herein. The overall layout shown in FIG.1 provides a hardware, aka—machine basis for deploying the varioussystems and methods described herein.

FIG. 2 is a simple diagram 20 illustrating the compartmentalization ofcurrent product information among different forums. In particular,researcher 21, in seeking information on a particular product for hisexperiment may consult a fellow scientist 22 for recommendations.However, because of conditions that may be different from the fellowscientist's 22, or the need for more sample data, etc., the researcher21 may need more information or seek other types of products, and soforth. Thus, the researcher 21 will typically expend many valuable hoursor weeks (and money) researching through public information repositoriesor websites 23, blogs 24, subscription-based services 25, privateorganizations 27, vendors 29 a-c, and so forth.

As apparent from the solid arrows in FIG. 2, there are many sources ofinformation (mostly conveyed through hardware channels—Internet,computers, etc.) that the researcher 21 must wade through to make aproper assessment of a product's relevance and use to his experiment.Similarly, vendors 29 a-c or manufacturers of the products must expendan enormous amount of energy and resources informing and advertising tothe various forums 22-27. While FIG. 2 is a rudimentary overview of atypical researcher's approach to finding product information, and doesnot describe all the possible avenues that a researcher may exploit, itis instructive in that it shows the breadth of difficulty a researcheris faced with when attempting to investigate a suitable product for hisexperiment.

FIG. 3 is a high level block diagram illustrating an exemplary systemand approach to addressing the deficiencies described in FIG. 2. Inaccordance with the various embodiments described herein, a centralizedor focal point of information 36 collation and retrieval can begenerated by mining the various resources 32-36 that the researcher 31would otherwise perform through a manual procedure. Specifically, anautomatic mining of data/product information can be facilitated, usingthe various mechanisms, hardware, software and coordination describedherein to efficiently identify, semantically tag, transform the soughtafter information to a format that is easily available to the researcher31. Of note here is the scenario where the same or some variant of thatproduct information is provided to the vendor 39, to enhance theirinformation/sales database, and/or to miscellaneous consumers (as onepossible example, web sites serving that community) 39 a of thatinformation.

The information and/or data obtained is facilitated in many respects bythe use of computer hardware to automate themining/retrieval/transformation of the embedded information by usingvarious elements of the layout shown in FIG. 1. Mining algorithms areused to sort through the enormous volumes of information that isavailable, with some algorithms being proprietary or publically known.In an exemplary embodiment, a searching and mining algorithm devised byParity Computing, Inc. has been used to demonstrate feasibility. Othermining techniques and procedures that are known in the public orproprietary may be used according to design implementation.

FIG. 4 is a high-level architectural drawing 40 illustrating variousflows of extracted/transformed information as well as their control aswell as options for commercial exploitation of that information. In thisdiagram, source of product information is shown as originating fromTrade Shows 41 (such as provided by Scientist Solutions, Inc.—SSI).However, it is understood that other sources of information may beutilized, such as by a Community Web Site 42 (such as provided byScientist Solutions, Inc.—SSI), Product Manufacturers 43, E-commercesite 44, and so forth. Therefore, while FIG. 4 describes one possibleset of originating data, other sets of originating data may be devisedwithout departing from the spirit and scope of this disclosure.

Returning to the process flow, from these sources 41-44, scientists'names/info can be obtained and product info/SKUs, for example. Adatabase 44 is populated with this information and organized to discoverthe product and obtain additional levels of organization/information, asaccording to design preference. Next, association methodologies 47 canbe applied to the database information to associate the various productinformation with scientific keywords or to determine a relatedness orranking of the products and so forth. A semantic refinement tool 48 isapplied to further mine and extract/correlate the product informationwith other factors or information or descriptors that would have abearing on a product (and scientist, if so desired). The results areapportioned into a refined product information database 49 and a refinedscientist information database 50. The process flow takes the refinedproduct information and feeds it to the E-commerce site 44 to assist inselling products, presenting referral links, advertising, and also tothe Marketing Intelligence Portal 51. As can be evident, a feedbackbetween the information originally provided by the E-commerce site 44and other gathered information can be utilized to enhance or update theinformation presented on the E-commerce site 44. This feedback would beautomatically generated by a computer or other automated apparatus.

Similarly, the refined scientist information would be forwarded to theMarketing Intelligence Portal 51, that would cull the information and,based on certain decision parameters, forward the culled information (orraw) to the Product Manufacturer 43. With this process flow ofinformation, the Product Manufacturer 43 can obtain higher “quality” ofscientists' use information as well as any other information that isgarnered from the exemplary processes. From the E-commerce site 44,information that is generated, etc. can be forwarded to various otherforums such as Affiliate Web Sites 52, Community Web Site 42 and theProduct Manufacturers 43, for referrals, for example. In turn, theProduct Manufacturers 43 can forward their renewed intelligence to theTrade Shows 41, for higher selling potential/advertising. With the newintelligence, the Product Manufacturers 43 can be better positioned foradvertising on the Community Web Site 42.

With this so-called cross pollination of information that is generatedby the exemplary processes and mining of product/scientist information,the difficulties demonstrated in FIG. 2 can be minimized andintelligence that hereto was not available for the different consumers(e.g., scientists, manufacturers, web sites, communities, etc.) can bemade available with a higher degree of accuracy and relevance.Additionally, for the manufacturers of various products, with thisintelligence, they can become more focused with their advertising andsave advertising costs. All of these features are implemented in variousdegrees using an automated process through the use of computers andsoftware running on the computer(s), performing the assorted levels ofinformation extraction, correlation, transformation, tagging, semanticanalysis, and so forth. As a point of emphasis, these various processesrequire computers to process the enormous amounts of information andtherefore, cannot be implemented by an individual.

FIG. 5 is diagram showing an example of exemplary keyword associationsthat facilitate the semantic enrichment process. This diagram isinstructive in showing how various levels of information can beextracted/mined from the initial set of data provided by the productname: XYZ. Many of the correlations that are made will be based ondatabases formulated to provide intelligence pairing of keywords with aproduct. That is, for a known product XYZ, the association ofTransfection, Cell Culture, siRNA, etc., may be automatically performed,while additional associations may occur as more knowledge of a productis obtained. It is noteworthy to recognize that the associations for theproduct XYZ can be mapped with a topology for efficient tagging, etc.

FIG. 6 is a detailed flow diagram illustrating the exemplary entry,semantic enrichment, and data display of a life science product. Thisdiagram is another variation of the process flow and mechanismsdescribed in FIG. 4 and is instructive in showing how differentarrangements of input and output can be generated for maximizing theobtained information's value. In this example, the primary informationis the product names/SKUs, which can be sourced from Affiliate Web Sites62, E-commerce Site(s) 64, Community Site(s) 66, and ProductManufacturers 68. The obtained product names/SKUs can then be associatedwith keyword scientific lists 69 and a semantic refinement/associationcan be performed 71. The resulting information is stored in adatabase(s) 72 and distributed to the same or another E-commerce site 74or Product Manufacturer(s) 76. The E-commerce site can further providethe mined and transformed information to Community Site(s) 78 and/orTrade Show(s) 80.

FIG. 7 is a flow diagram illustrating the entry, semantic enrichment,and data display of life scientist consumer data. Scientists' names andcontact information can be automatically obtained via Affiliate WebSites 82, E-commerce Sites 84, Community Sites 86, and ProductManufacturers 88, using software or other automated procedures.Thereafter, the obtained information can be associated with keywordscientific lists 89 and then operated upon using semantic refinement andassociation tools, such as provided by Parity Computing, Inc. Next, themanipulated information can be stored in a refined database 92 anddistributed to the various clients 94, 96 and 98, as similarly describedabove.

FIGS. 8A-B are screen shots of a representative refined data set for anexample product (Lipofectamine 2000) for semantic enrichment usingprocesses for information extraction/semantic analysis, etc. Evident arethe different levels of information/metrics that are obtained, such asreviews, top citing publications, web mentions, topic summary, relatedtopics, other products, related products, competing products, topexperts, market share, trends, Science 2.0 citations, and so forth. Thisexample provides a good illustration of the breadth of information thatis automatically afforded the user that prior to this invention wouldnot have been available. Also, because of the organization shown, theuser can more readily make an informed decision for a given product, aswell as a scientist's use of that product.

FIG. 9 is a screen display illustrating an HTML displaying relevantproduct data for the example product MiniOpticon Real-Time PCR DetectionSystem in a community or affiliate web site. Based on the arrangementbetween the sourcing agency (SSI) and the affiliate web site, differentlevels of information may be showcased on the community/affiliate website. For example, in FIG. 9, product summary, top citations, reviews,related products, and experimental work flow is exhibited to the user.With a “Buy Now” feature embedded within this web view (for example, inthe Embed Widget window), the user can now purchase the product armedwith more knowledge than what would be ordinarily available. Thus,manufacturers of this product would benefit from the increased awarenessand statistical knowledge that is collected and distributed.

FIG. 10 is a screen display illustrating an HTML page displaying dataderived from non-obvious connections to promote products within a blogenvironment, and shows another forum that would benefit from theexemplar systems and methods described herein. For example, some of theareas available in the blog environment may be configured for promotingthe exemplary processes/SSI garnered information. One such method may besimilar to Google's™ approach to advertising information/links relatedto the blogger's information, wherein the products could be hyperlinkedin these areas.

In view of the above examples, subscription-based revenue could begenerated or other forms of revenue (Ad-based, click-based, etc.) forthe sourcing entity (e.g., SSI). Thus, by aggregating information andextracting statistics and information through mining and semanticassociations, that would not be otherwise practically available,increased sales can be obtained as well as streamlining the productreview and research cycle that scientists must perform.

It should be understood that the steps of a method or algorithmdescribed in connection with the embodiments disclosed herein may beembodied directly in hardware, in a software module executed by aprocessor, or in a combination of the two. A software module may residein RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory,registers, hard disk, a removable disk, a CD-ROM, or any other form ofstorage medium known in the art. An exemplary storage medium is coupledto the processor such that the processor can read information from, andwrite information to, the storage medium. In the alternative, thestorage medium may be integral to the processor. The processor and thestorage medium may reside in an ASIC.

It is understood that the specific order or hierarchy of steps in theprocesses disclosed is an example of exemplary approaches. Based upondesign preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged while remainingwithin the scope of the present disclosure.

The previous description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentdisclosure. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the disclosure. Thus, the present disclosure is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

What is claimed is:
 1. A computerized method for mining distributedscientific data for enriched product or contact valuation, comprising:obtaining distributed scientific data, presented in scientificpublications, in scientific forums, peer-reviewed scientificpublications and online resources and contact data from a plurality ofindependently segregated sources using an automatic searching tool, saiddistributed scientific data comprising data from subscription basedresources and data from non-subscription based resources; using acomputer to associate the obtained data relevant to at least one of thescientific data and contact data using an automatic association tool,and storing at least an aspect of the obtained data to provide asemantic association, using the data, as stored obtained dataassociations; applying a computerized semantic refinement algorithm tothe associated data to obtain refined data, the applying of thecomputerized semantic refinement algorithm including miningrelationships to search objects by linking data from scientificpublications and other distributed scientific data from the subscriptionbased resources and the non-subscription based resources as severalpossible resource examples, and using the resulting mined information toformulate relationships between the search objects and the linked data,thereby enhancing correlation and information transparency, thealgorithm embodied directly in a software module resident in a storagemedium, the storage medium is coupled to the processor such that theprocessor can read information from, and write information to, thestorage medium; applying an algorithm function providing machinelearning by performing assorted levels of information extraction,correlation, transformation, tagging and semantic analysis; using thecomputer to parse the refined data to an applicable scientific databaseand an applicable contact database and outputting information from thecomputer for storage in database store, wherein the databases containconcise information regarding at least one of a scientific data summary,product summary, product review, top citing publications for theproduct, web mentions, topic summary, related topics, other products ina family, related products, competing products, names of top expertsusing the product, current market share, market share trends, andScience 2.0 citations; and using a semantic enrichment process togenerate new refinements of associations occurring from obtaining newknowledge by extracting or mining information from the stored obtaineddata associations.
 2. The method of claim 1, wherein the conciseinformation correlates at least one of product efficacy and product usewith the obtained data for efficient valuation thereof.
 3. The method ofclaim 1, further comprising providing concise information feedback to atleast one of a product manufacturer, public website, e-commerce site,community website, and trade show.
 4. The method of claim 1, furthercomprising obtaining the scientific data from scientific product data,wherein one of the individually segregated sources is at least one of asubscription based source, product manufacturer, and web-based sourceand wherein the individually segregated sources include reviews byproduct manufacturers and scientists.
 5. The method of claim 1, furthercomprising developing marketing intelligence based on the conciseinformation.
 6. The method of claim 1, further comprising displaying theconcise information in a format having at least one of a scientific datasummary, product summary, product review, top citing publications forthe scientific data or product, web mentions, topic summary, relatedtopics, other products in a family, related products, competingproducts, names of top experts using the product, current market share,market share trends, and Science 2.0 citations.
 7. The method of claim6, wherein the displaying includes displaying a web-based link to amanufacturer of a displayed product.
 8. The method of claim 6, whereinthe displaying includes displaying a list of scientific validations of aproduct.
 9. A system for mining distributed scientific data for enrichedproduct and/or contact valuation, comprising: a communication network; acomputerized device connected to the communication network, executinginstructions for executing the method of claim
 1. 10. The system ofclaim 9, wherein the concise information correlates at least one ofproduct efficacy and product use with the obtained data for efficientvaluation thereof.
 11. The system of claim 9, wherein the computerizeddevice further executes instructions for providing concise informationfeedback to at least one of a product manufacturer, public website,e-commerce site, community website, and trade show.
 12. The system ofclaim 9, further comprising the computerized device obtains thescientific data from scientific product data, wherein one of theindividually segregated sources is at least one of a subscription basedsource, product manufacturer, and web-based source, and wherein theindividually segregated sources include reviews by product manufacturersand scientists.
 13. The system of claim 9, wherein the computerizeddevice further executes instructions for developing marketingintelligence based on the concise information.
 14. The system of claim9, further comprising a display, wherein the computerized deviceexecutes instructions for displaying the concise information on thedisplay in a format having at least one of a product summary, productreview, top citing publications for the product, web mentions, topicsummary, related topics, other products in a family, related products,competing products, names of top experts using the scientific data orproduct, current market share, market share trends, and Science 2.0citations.
 15. The system of claim 14, wherein the computerized deviceexecutes instructions for displaying at least a web-based link to amanufacturer of a displayed product and a list of scientific validationsof a product.
 16. A system for mining distributed scientific data forenriched product or contact valuation, comprising: computer hardwaremeans for obtaining distributed scientific product data, presented inscientific publications, in scientific forums, peer-reviewed scientificpublications and online resources and contact data from a plurality ofindependently segregated sources from scientific publications and theInternet using an automatic searching tool, said distributed scientificproduct data comprising data from subscription based resources and datafrom non-subscription based resources; computer hardware means forassociating the obtained data relevant to at least one of the productand contact data using an automatic association tool, and storing atleast an aspect of the obtained data to provide a semantic association,using the data, and storing the semantic associations as stored obtaineddata associations; means for applying a computerized semantic refinementalgorithm to the associated data to obtain refined data, the applying ofthe computerized semantic refinement algorithm including miningrelationships to search objects by linking data from scientificpublications and other distributed scientific product data from thesubscription based resources and the non-subscription based resources asseveral possible resource examples, and using the resulting minedinformation to formulate relationships between the search objects andthe linked data, thereby enhancing correlation and informationtransparency, the algorithm embodied directly in a software moduleresident in a storage medium, the storage medium is coupled to theprocessor such that the processor can read information from, and writeinformation to, the storage medium; an algorithm function providingmachine learning by performing assorted levels of informationextraction, correlation, transformation, tagging and semantic analysis;computer hardware means for automatically parsing the refined data to anapplicable scientific product database and an applicable contactdatabase for storage in database store, wherein the databases containconcise information regarding at least one of a product summary, productreview, top citing publications for the product, web mentions, topicsummary, related topics, other products in a family, related products,competing products, names of top experts using the product, currentmarket share, market share trends, and Science 2.0 citations; and asemantic enrichment process for generating new refinements ofassociations occurring from obtaining new knowledge by extracting ormining information from the stored obtained data associations.
 17. Thesystem of claim 16, further comprising means for providing conciseinformation feedback to at least one of a product manufacturer, publicwebsite, e-commerce site, community website, and trade show.
 18. Thesystem of claim 16, further comprising means for developing marketingintelligence based on the concise information.
 19. The system of claim16, further comprising means for displaying the concise information in aformat having at least one of a product summary, product review, topciting publications for the product, web mentions, topic summary,related topics, other products in a family, related products, competingproducts, names of top experts using the product, current market share,market share trends, and Science 2.0 citations.
 20. A non-transitorycomputer-readable medium comprising: programming code for obtainingdistributed scientific product data, presented in scientificpublications, in scientific forums, peer-reviewed scientificpublications and online resources and contact data from a plurality ofindependently segregated sources using an automatic searching tool, saiddistributed scientific product data comprising data from subscriptionbased resources and data from non-subscription based resources;programming code for associating the obtained data relevant to at leastone of the product and contact data using an automatic association tool,and storing at least an aspect of the obtained data to provide asemantic association, using the data, and storing the semanticassociations as stored obtained data associations; programming code forapplying a computerized semantic refinement algorithm to the associateddata to obtain refined data, the applying of the computerized semanticrefinement algorithm including mining relationships to search objects bylinking data from scientific publications and other distributedscientific product data from the subscription based resources and thenon-subscription based resources as several possible resource examples,and using the resulting mined information to formulate relationshipsbetween the search objects and the linked data, thereby enhancingcorrelation and information transparency, the algorithm embodieddirectly in a software module resident in a storage medium, the storagemedium is coupled to the processor such that the processor can readinformation from, and write information to, the storage medium;programming code for an algorithm function providing machine learning byperforming assorted levels of information extraction, correlation,transformation, tagging and semantic analysis; and programming code forautomatically parsing the refined data to an applicable scientificproduct database and an applicable contact database for storage indatabase store, wherein the databases contain concise informationregarding at least one of a product summary, product review, top citingpublications for the product, web mentions, topic summary, relatedtopics, other products in a family, related products, competingproducts, names of top experts using the product, current market share,market share trends, and Science 2.0 citations; and programming code forusing a semantic enrichment process to generate new refinements ofassociations occurring from obtaining new knowledge by extracting ormining information from the stored obtained data associations, whereinthe concise information correlates at least one of product efficacy andproduct use with the obtained data for efficient valuation thereof.